Registration between volumetric images (data volumes) can involve mapping locations in 3-dimensional (3D) images to homologous locations in other 3D images. The capability of automatically performing elastic (or non-rigid) registration between volumetric images has many potential benefits, especially in medical applications where 3D images of patients are routinely acquired using CT, MRI, PET, SPECT and ultrasound scanners. Known methods for automatic 3D registration are described in U.S. Pat. No. 6,909,794 and WO2006118548 and references thereof and in the book “Handbook of Biomedical Image Analysis, volume 3: Registration Models” by Suri et al. (2005) and references thereof.
Many robust 3D registration methods are iterative: one or more initial guesses are generated and then refined by a directed search for mapping parameters that maximize a similarity metric. Typically a large amount of computation needs to be performed per iteration to derive new mapping vectors, resample at least one of the 3D images on a curved grid and compute a global similarity function. The number of initial starting points (guesses) and optimization iterations required to ensure success increases exponentially when the degree of similarity between the contents of the image volumes is low under a rigid mapping, for example when images from a patient are registered to an annotated atlas created using images of one or more other patients. Elastic 3D registration, therefore, has had only limited usefulness in clinical practice to date.